Weather Extremes (Precipitation - Model Inadequacies) -- Summary
One of the basic predictions of atmospheric general circulation models (GCMs) is that the planet's hydrologic cycle will intensify as the world warms, leading to an increase in both the frequency and intensity of extreme precipitation events. In an early review of the subject, Walsh and Pittock (1998) reported "there is some evidence from climate model studies that, in a warmer climate, rainfall events will be more intense," and that "there is considerable evidence that the frequency of extreme rainfall events may increase in the tropics." Upon further study, however, they were forced to conclude that "because of the insufficient resolution of climate models and their generally crude representation of sub-grid scale and convective processes, little confidence can be placed in any definite predictions of such effects."

Two years later, Lebel et al. (2000) compared rainfall simulations produced by a GCM with real-world observations from West Africa for the period 1960-1990. Their analysis revealed that the model output was affected by a number of temporal and spatial biases that led to significant differences between observed and modeled data. The simulated rainfall totals, for example, were significantly greater than what was typically observed, exceeding real-world values by 25% during the dry season and 75% during the rainy season. In addition, the seasonal cycle of precipitation was not well simulated, as the researchers found that the simulated rainy season began too early and that the increase in precipitation was not rapid enough. Shortcomings were also evident in the GCM's inability to accurately simulate convective rainfall events, as it typically predicted far too much precipitation. Furthermore, it was found that "interannual variability [was] seriously disturbed in the GCM as compared to what it [was] in the observations." As for why the GCM performed so poorly in these several respects, Lebel et al. gave two main reasons. They said the parameterization of rainfall processes in the GCM was much too simple and that the spatial resolution was much too coarse.

Following the passage of an additional three years, Woodhouse (2003) generated a tree-ring-based history of snow water equivalent (SWE) characteristic of the first day of April for each year of the period 1569-1999 for the drainage basin of the Gunnison River of western Colorado, USA. Then, because "an understanding of the long-term characteristics of snowpack variability is useful for guiding expectations for future variability," as she phrased it, she analyzed the reconstructed SWE data in such a way as to determine if there was there anything unusual about the SWE record of the 20th century, which hundred-year period is claimed by climate alarmists to have experienced a warming that was unprecedented over the past two millennia.

So did Woodhouse find anything unusual? Yes, she did. She found that "the twentieth century is notable for several periods that lack [our italics] extreme years." Specifically, she determined that "the twentieth century is notable for several periods that contain few or no extreme years, for both low and high SWE extremes," and she reports that "the twentieth century also contains the lowest percent of extreme low SWE years." These results, of course, are in direct contradiction of what state-of-the-art GCMs typically predict should occur in response to global warming; and their failure in this regard is especially damning, knowing it occurred during a period of global warming that is said by many have been the most significant of the past 20 centuries.

Two years later, and as a result of the fact that the 2004 summer monsoon season of India experienced a 13% precipitation deficit that was not predicted by any of the empirical or dynamical models regularly used in making rainfall forecasts, Gadgil et al. (2005) performed an historical analysis of the models' forecast skill over the period 1932-2004. Interestingly, and despite numerous model advancements and an ever-improving understanding of monsoon variability, they found that the models' skill in forecasting the Indian monsoon's characteristics had not improved since the very first versions of the models were applied to the task some seven decades earlier.

In the case of the empirical models Gadgil et al. evaluated, large differences were generally observed between monsoon rainfall measurements and model predictions. In addition, the models often failed to correctly predict even the sign of the precipitation anomaly, frequently predicting excess rainfall when drought occurred and drought when excess rainfall was received.

The dynamical models fared even worse. In comparing observed monsoon rainfall totals with simulated values obtained from 20 state-of-the-art GCMs and a supposedly superior coupled atmosphere-ocean model, Gadgil et al. report that not a single one of these many models was able "to simulate correctly the interannual variation of the summer monsoon rainfall over the Indian region." And as with the empirical models, the dynamical models also frequently failed to correctly capture even the sign of the observed rainfall anomalies. In addition, the researchers report that Brankovic and Molteni (2004) attempted to model the Indian monsoon with a much higher-resolution GCM, but that its output also proved to be "not realistic."

Consequently, and in spite of the billions of dollars that have been spent by the United States alone on developing and improving climate models, taxpayers have achieved essentially no return on their investment in terms of the models' ability to correctly simulate one of the largest and most regionally-important of earth's atmospheric phenomena -- the tropical Indian monsoon. After more than 70 years of trying to remake the models into better predictive tools, one would surely have expected some improvement in this regard, even if only by accident. That there has been absolutely none is a sad commentary indeed on the state of the climate modeling enterprise.

Advancing one more year in time, Lau et al. (2006) considered the Sahel drought of the 1970s-90s to provide "an ideal test bed for evaluating the capability of CGCMs [coupled general circulation models] in simulating long-term drought, and the veracity of the models' representation of coupled atmosphere-ocean-land processes and their interactions." Hence, they decided to "explore the roles of sea surface temperature coupling and land surface processes in producing the Sahel drought in CGCMs that participated in the twentieth-century coupled climate simulations of the Intergovernmental Panel on Climate Change [IPCC] Assessment Report 4," in which the 19 CGCMs "are driven by combinations of realistic prescribed external forcing, including anthropogenic increase in greenhouse gases and sulfate aerosols, long-term variation in solar radiation, and volcanic eruptions."

In performing this analysis, the climate scientists found, in their words, that "only eight models produce a reasonable Sahel drought signal, seven models produce excessive rainfall over [the] Sahel during the observed drought period, and four models show no significant deviation from normal." In addition, they report that "even the model with the highest skill for the Sahel drought could only simulate the increasing trend of severe drought events but not the magnitude, nor the beginning time and duration of the events." Consequently, since all 19 of the CGCMs employed in the IPCC's Fourth Assessment Report failed to adequately simulate the basic characteristics of "one of the most pronounced signals of climate change" of the past century -- as defined by its start date, severity and duration -- the results of this "ideal test" for evaluating the models' capacity for accurately simulating "long-term drought" and "coupled atmosphere-ocean-land processes and their interactions" would almost mandate that it would be unwise to rely on their output as a guide to the future, especially when the tested models were "driven by combinations of realistic prescribed external forcing" and they still could not properly simulate the past.

During the following year of 2007, a number of other pertinent papers appeared. In an intriguing report in Science, Wentz et al. (2007) noted that the Coupled Model Intercomparison Project, as well as various climate modeling analyses, predicted an increase in precipitation on the order of one to three percent per °C of surface global warming. Hence, they decided to see what had happened in the real world in this regard over the prior 19 years (1987-2006) of supposedly unprecedented global warming, when data from the Global Historical Climatology Network and satellite measurements of the lower troposphere indicated there had been a global temperature rise on the order of 0.20°C per decade.

Using satellite observations obtained from the Special Sensor Microwave Imager (SSM/I), the four Remote Sensing Systems scientists derived precipitation trends for the world's oceans over this period; and using data obtained from the Global Precipitation Climatology Project that were acquired from both satellite and rain gauge measurements, they derived precipitation trends for earth's continents. Appropriately combining the results of these two endeavors, they derived a real-world increase in precipitation on the order of 7% per °C of surface global warming, which is somewhere between 2.3 and 7 times larger than what is predicted by state-of-the-art climate models.

How was this horrendous discrepancy to be resolved?

Based on theoretical considerations, Wentz et al. concluded that the only way to bring the two results into harmony with each other was for there to have been a 19-year decline in global wind speeds. But when looking at the past 19 years of SSM/I wind retrievals, they found just the opposite, i.e., an increase in global wind speeds. In quantitative terms, in fact, the two results were about as opposite as they could possibly be, as they report that "when averaged over the tropics from 30°S to 30°N, the winds increased by 0.04 m s-1 (0.6%) decade-1, and over all oceans the increase was 0.08 m s-1 (1.0%) decade-1," while global coupled ocean-atmosphere models or GCMs, in their words, "predict that the 1987-to-2006 warming should have been accompanied by a decrease in winds on the order of 0.8% decade-1."

In discussing these embarrassing results, Wentz et al. correctly state that "the reason for the discrepancy between the observational data and the GCMs is not clear." They also rightly state that this dramatic difference between the real world of nature and the virtual world of climate modeling "has enormous impact," concluding that the questions raised by the discrepancy "are far from being settled." And until these "enormous impact questions" are settled, we wonder how anyone could conceivably think of acting upon the global energy policy prescriptions of the likes of Al Gore and James Hansen, who speak and write as if there was little more to do in the realm of climate-change prediction than a bit of fine-tuning.

In another intriguing bit of research, Allan and Soden (2007) quantified trends in precipitation within ascending and descending branches of the planet's tropical circulation and compared their results with simulations of the present day and projections of future changes provided by up to 16 state-of-the-art climate models. The precipitation data for this analysis came from the Global Precipitation Climatology Project (GPCP) of Adler et al. (2003) and the Climate Prediction Center Merged Analysis of Precipitation (CMAP) data of Xie and Arkin (1998) for the period 1979-2006, while for the period 1987-2006 they came from the monthly mean intercalibrated Version 6 Special Sensor Microwave Imager (SSM/I) precipitation data described by Wentz et al. (2007).

So what did the researchers learn?

Allan and Soden report that "an emerging signal of rising precipitation trends in the ascending regions and decreasing trends in the descending regions are detected in the observational datasets," but that "these trends are substantially larger in magnitude than present-day simulations and projections into the 21st century," especially in the case of the descending regions. More specifically, they state that, for the tropics, "the GPCP trend is about 2-3 times larger than the model ensemble mean trend, consistent with previous findings (Wentz et al., 2007) and also supported by the analysis of Yu and Weller (2007)," who additionally contend that "observed increases of evaporation over the ocean are substantially greater than those simulated by climate models." What is more, Allan and Soden note that "observed precipitation changes over land also appear larger than model simulations over the 20th century (Zhang et al., 2007)."

What is one to make of this conflict between models and measurements?

Noting that the difference between the two "has important implications for future predictions of climate change," Allan and Soden say "the discrepancy cannot be explained by changes in the reanalysis fields used to subsample the observations but instead must relate to errors in the satellite data or in the model parameterizations [our italics]." This same dilemma was also faced by Wentz et al. (2007); and they too stated that the resolution of the issue "has enormous impact," but likewise concluded that the questions raised by the discrepancy "are far from being settled."

To us, the issue seems a bit less difficult. Given a choice between model simulations and observational reality, we will cast our lot with the latter every chance we get. Granted, this choice implies a huge problem with the former. But why should that be a surprise to anyone? The earth, with its oceans and atmosphere, and its myriad life forms, is a most complex place; and to believe that we have condensed all of its many climate-related phenomena -- many of which are shrouded in mystery, and some of which may even remain undetected -- to a set of equations that rigorously define our climatic future in response to an increase in anthropogenic CO2 emissions, seems to us to be irrationality incarnate.

In a contemporaneous study, L'Ecuyer and Stephens (2007) wrote that "our ability to model the climate system and its response to natural and anthropogenic forcings requires [our italics] a faithful representation of the complex interactions that exist between radiation, clouds, and precipitation and their influence on the large-scale energy balance and heat transport in the atmosphere," further noting that "it is also critical to assess [model] response to shorter-term natural variability in environmental forcings using observations." In the spirit of this logical philosophy, the two researchers decided to use multi-sensor observations of visible, infrared and microwave radiance obtained from the Tropical Rainfall Measuring Mission satellite for the period running from January 1998 through December 1999, in order to evaluate the sensitivity of atmospheric heating -- and the factors that modify it -- to changes in east-west sea surface temperature gradients associated with the strong 1998 El Niño event in the tropical Pacific, as expressed by the simulations of nine general circulation models of the atmosphere that were utilized in the Intergovernmental Panel on Climate Change's most recent Fourth Assessment Report. This protocol, in their words, "provides a natural example of a short-term climate change scenario in which clouds, precipitation, and regional energy budgets in the east and west Pacific are observed to respond to the eastward migration of warm sea surface temperatures," which is somewhat akin to the natural experiment approach of Idso (1998).

So what did they learn from this exercise?

L'Ecuyer and Stephens report that "a majority of the models examined do not reproduce the apparent westward transport of energy in the equatorial Pacific during the 1998 El Niño event." They also state that "the intermodel variability in the responses of precipitation, total heating, and vertical motion is often larger than the intrinsic ENSO signal itself, implying an inherent lack of predictive capability in the ensemble with regard to the response of the mean zonal atmospheric circulation in the tropical Pacific to ENSO." In addition, they say that "many models also misrepresent the radiative impacts of clouds in both regions [the east and west Pacific], implying errors in total cloudiness, cloud thickness, and the relative frequency of occurrence of high and low clouds." In light of these much-less-than-adequate findings, therefore, the two researchers concluded that "deficiencies remain in the representation of relationships between radiation, clouds, and precipitation in current climate models," and they say that these deficiencies "cannot be ignored when interpreting their predictions of future climate."

In one final paper from the same year, Lin (2007) states that "a good simulation of tropical mean climate by the climate models is a prerequisite [our italics] for their good simulations/predictions of tropical variabilities and global teleconnections," but that "unfortunately, the tropical mean climate has not been well simulated by the coupled general circulation models (CGCMs) used for climate predictions and projections [our italics]," noting that "most of the CGCMs produce a double-intertropical convergence zone (ITCZ) pattern," and acknowledging that "a synthetic view of the double-ITCZ problem is still elusive."

To explore the nature of this problem in greater depth, and to hopefully make some progress in resolving it, Lin analyzed tropical mean climate simulations of the 20-year period 1979-99 provided by 22 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) CGCMs, together with concurrent Atmospheric Model Intercomparison Project (AMIP) runs from 12 of them.

This work revealed, in Lin's words, that "most of the current state-of-the-art CGCMs have some degree of the double-ITCZ problem, which is characterized by excessive precipitation over much of the Tropics (e.g., Northern Hemisphere ITCZ, Southern Hemisphere SPCZ [South Pacific Convergence Zone], Maritime Continent, and equatorial Indian Ocean), and often associated with insufficient precipitation over the equatorial Pacific," as well as "overly strong trade winds, excessive LHF [latent heat flux], and insufficient SWF [shortwave flux], leading to significant cold SST (sea surface temperature) bias in much of the tropical oceans," while additionally noting that "most of the models also simulate insufficient latitudinal asymmetry in precipitation and SST over the eastern Pacific and Atlantic Oceans," further stating that "the AMIP runs also produce excessive precipitation over much of the Tropics including the equatorial Pacific, which also leads to overly strong trade winds, excessive LHF, and insufficient SWF," which suggests that "the excessive tropical precipitation is an intrinsic error of the atmospheric models." And if that is not enough, Lin adds that "over the eastern Pacific stratus region, most of the models produce insufficient stratus-SST feedback associated with insufficient sensitivity of stratus cloud amount to SST."

With the solutions to all of these long-standing problems continuing to remain "elusive," and with Lin suggesting that the sought-for solutions are in fact prerequisites for "good simulations/predictions" of future climate, there is significant reason to conclude that current state-of-the-art CGCM predictions of CO2-induced global warming ought not be considered all that reliable. And to cite these predictions as the primary basis for totally revamping the way the world obtains the energy used to power modern societies, would seem to us to be the height of folly.

Allan, R.P. and Soden, B.J. 2007. Large discrepancy between observed and simulated precipitation trends in the ascending and descending branches of the tropical circulation. Geophysical Research Letters34: 10.1029/2007GL031460.

L'Ecuyer, T.S. and Stephens, G.L. 2007. The tropical atmospheric energy budget from the TRMM perspective. Part II: Evaluating GCM representations of the sensitivity of regional energy and water cycles to the 1998-99 ENSO cycle. Journal of Climate20: 4548-4571.